The practice of dispensing medication via a pharmacy has undergone radical change in recent years, with a paradigm shift from small, independent pharmacies to regional and national networks of Publicly-held Corporate Pharmacies (“PCPs”). The advent of PCPs was in response to a desire by the industry to minimize the cost of drug therapy while maximizing profitability. Under the PCP system, much of the decision-making power is shifted from health care providers to an administrative organization that establishes standards of care, standardizes methods of delivering care, and evaluates the outcomes of given care. PCPs work to minimize costs and maintain profits through a variety of means, including volume purchases, quality control, formulary lists of preferred medications, discounts for movement of market share, and negotiated healthcare fees.
Since PCPs focus on reducing the cost of health care and maintaining profits, there is a high degree of interest in acquiring as much historical and timely ongoing data as possible regarding medication use and benefit, comparative costs of alternate therapies, and patient demographics. This information can be collected, organized and stored in a database or “data warehouse” for use in a wide variety of medical and economic analyses. A data warehouse is a process by which large quantities of related data from many operational systems is merged into a single standard repository to provide an integrated information view based on logical queries. Types of logical queries may relate to “data mining,” which can be defined as a process of data selection, exploration and building models using vast data stores to discover previously unknown relationships and patterns. Other queries may be in support of clinical research on a particular medication or malady.
PCPs regularly conduct a number of data reviews as part of the quality control process. In general, these reviews include a Drug Utilization Review (“DUR”) and a Drug Usage Evaluation (“DUE”). These reviews seek to establish best practices for maximizing patient benefit, optimizing PCP expenditures, and maintaining profitability by minimizing the number of different medications used for the same treatments and optimizing market share for the medicines used.
As part of the cost-containment process, a PCP typically negotiates price discounts and other incentives with its source of prescription medications, the pharmaceutical companies. These discounts are often based on the volume of the companies' products used by the PCP and gains in the pharmaceutical companies' market share that are attributable to the PCP. This creates a need for the PCP to obtain accurate actual consumption data for the medications dispensed by its network of pharmacies. Unfortunately, this data is not always readily available. This is due in part to the fact that a particular prescription may be partially fulfilled in several “transactions.” A transaction typically includes such information as patient name, prescribing physician, medication name, prescription quantity, quantity dispensed, pharmacist's name, and date of fulfillment. A transaction may also include usage information, such as one or more returns of part or all of a prescription. In addition, the prescription may be fulfilled with generics or medications from several companies in separate transactions. These variables make it difficult to track actual consumption of a particular company's product for a given prescription. When computing actual medication consumption, the errors introduced by summing individual transaction records are magnified when large numbers of transactions are involved, creating an unacceptable margin of error. Further, manual extraction of dispensing and usage data is both time-consuming and labor-intensive. There is a need for a timely method that can assimilate prescription data longitudinally from pharmacy transaction data such that the prescription data can be more accurately accumulated and analyzed to aid the PCP decision-making and cost negotiation processes.